Urban Flood Events Research Articles

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model

Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km2). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.

Assessing Reliability, Resilience and Vulnerability of Water Supply from SuDS

In recent decades, the impacts of urbanization on the hydrological cycle have led to an increase in the frequency and magnitude of urban flooding events, and this is also amplified by the effects of climate change. Sustainable Drainage Systems (SuDS) provide a revolutionary change in this field, improving the sustainability and resilience of cities. This research explores the integration of different SuDS with the aim of significantly reducing both the flow volume and celerity of floods in a residual urban catchment area of the metropolitan city of Querétaro (Mèxico), where extreme rainfall frequently occurs. This catchment is a representative suburb of urban pressure and environmental degradation problems. Currently, managing storm water under climate uncertainty through a multi-disciplinary approach is a major concern in this urban area. A 1D–2D coupling model of shallow water equations, the finite volume method, an unstructured meshing method, and a hybrid parallel computing application defined the optimal configuration of SuDS at catchment scale to reduce the flood vulnerability in Querétaro. Specifically, in this paper, we explore the management issues of the proposed SuDS configuration that acts as a water resource system with multiple purposes. A generic simulation model called MODSIM was applied to simulate the designed urban drainage system under a balanced IPCC future climate scenario in terms of reliability, resilience and vulnerability against water scarcity. The proposed hierarchical Reliability–Resilience–Vulnerability approach appears to be effective in evaluating the system performance, showing that the complete satisfaction of non-essential water uses in Querétaro can be assured at a 65% rate of reliability for a large range of reservoir storage conditions.

Evaluation of the number of events’ influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method

Rainfall-runoff data in drainage systems in urban areas is the essential input variable for urban hydrological modeling. Obtaining high quality and sufficient size of urban flood events is always difficult due to the inconvenient underground observation and the untimely capture of the rapid rising and recession stages of urban runoff. It largely constrains the efficiency of model calibration and brings large uncertainty in urban rainfall-runoff simulation. Therefore, the evaluation of the number of events’ influence on model performance and uncertainty is of great significance, which can provide useful information to help the decision makers select sufficient useful observation data for model calibration with relatively fewer events. In this study, we constructed a comprehensive method of behavioral parameter ranking of multi-events (BPROME) based on coupling the Generalized Likelihood Uncertainty Estimation (GLUE) algorithm and the Storm Water Management Model (SWMM). The results in the two small demonstration areas (named Case #A and Case #B) in Shenzhen city of China proved the good performance of the BPROME in uncertainty assessment in urban areas. We found the model uncertainty (average bandwidth (B) and average deviation amplitude (D)) and model performance (containing Ratio (CR) and the maximum value of behavioral samples’ NSE (NSEmax)) became stable at a certain number of events in both two case areas. The B and D decrease and the CR and NSEmax increase as the number of event increases. In particular, the model performance and uncertainty reach their appropriate state concerning the limited observations at a certain range of numbers of events (both three to five in our two case areas). Our results demonstrate the potential influence of the numbers of events for the urban rainfall-runoff modeling calibration which can balance the model efficiency and data collection cost (the number of input rainfall-runoff data). The findings could help decision-makers seek a trade-off between data investment and acceptable model performance.

The influence of microtopography to road inundation caused by extreme flood

Microtopography plays a critical role in road inundation during urban flood events. The microtopography in this paper was defined as terrain-scale features that encompass surface roughness, slope, road network and urban building layout. This paper aims to explore the mechanism of depression storage and road inundation under different microtopography. Simulations under 4 rainfall intensities (144.0– 182.88 mm/h) and 14 slope combinations (four transverse slope and five longitudinal slope) were implemented in an 800 by 70 cm local model. The correlation heat map directly reflected that longitudinal slope had higher influence on drainage than other factors. Then real topographical and hydrological data was applied to predict road inundation with five different extreme rainfall events in Jiangning District (Nanjing City, China). The microtopography characteristics of frequent inundation road were extracted, which further verified the conclusions of the local model. Results show that: the microtopography depressions drainage process could be divided into six main stages: filling stage, interaction stage, unstable drainage stage, stable flow stage, drainage stage and stage of drainage end. Water was stored on depressions of road, and the storage volume and discharge efficiency were affected by the surface relief and slope. The emergence of slope provided an altered path and power for water drainage. Only 0.3 % slope could contribute a 28.4 % to discharge efficiency. Upon comparation, the best combination for drainage was 2.0 % transverse slope with 3.0 % longitudinal slope. These findings provided meaningful insights and perspectives for urban flood hazard mitigation and were a more detailed reference for road design.

Application of graph neural networks to forecast urban flood events: the case study of the 2013 flood of the Bow River, Calgary, Canada

ABSTRACT Mitigating the harmful effects of flooding is essential in the current climate change and urban growth scenario, where these natural phenomena are expected to occur more often. Addressing this problem, we present a novel graph-based forecasting model for predicting urban flooding and showcase its application in the Bow River in the City of Calgary, Alberta, Canada. The proposed model is based on graph theory and deep learning paradigms and was used to forecast flooding occurrences up to 24 h ahead. Our new approach developed the SAGE algorithm, a hybrid learning and planning approach that accurately predicted floodings for various analyzed forecasting horizons, increasing the forecasting performance by up to 44% compared to the persistence model. The SAGE model also returned superior or competitive results compared to other models in the literature. The superiority of the SAGE model was highlighted for longer forecast horizons of 24 h ahead, where it reached a maximum improvement of 79%, suggesting its superiority in capturing spatiotemporal information in the dataset. These results indicate that the SAGE approach is a cutting-edge tool for flooding forecasting, and it could be used to develop early flood warning systems to reduce potential flooding impacts.

Acquisition of disability after age 50 following extreme urban coastal flooding events in India

Acquisition of disability after age 50 following extreme urban coastal flooding events in India

Numerical simulation and characteristic analysis of flood intrusion into streets and buildings

In recent years, the study of urban floods has gained significant importance due to the impacts of climate change and rapid urbanization. A crucial task in accurately reproducing real-world urban flood events lies in enhancing the modeling capability to incorporate the entry of floodwaters into buildings through openings. This study presents a validated two-dimensional (2D) model based on the Shallow Water Equations (SWEs) to extend the numerical simulations beyond the analysis of water entering a single building to a cluster of buildings. The results confirm the feasibility of incorporating floodwater entering buildings processes in future urban flood simulations, thereby enhancing the realism of the outcomes. Moreover, this research delves into the characteristics of water depth and flow patterns not only on the streets but also inside the buildings. The investigation reveals that building openings play a vital role in the transportation and redistribution of floodwaters within the street network. Furthermore, variations in the rates of water level rise and recession inside buildings are observed, highlighting the influence of opening configurations and building locations. Additionally, a dynamic assessment of pedestrian risk throughout the flood duration is conducted, providing valuable insights and recommendations for disaster prevention and mitigation strategies during urban flood events. It serves as a valuable resource for urban planners and policymakers involved in the development of resilient cities in the face of increasing urban flood challenges.

A two-dimensional hydrodynamic urban flood model based on equivalent drainage of manholes

Abstract Numerical simulations of urban flood events are of great significance in flood control and disaster reduction. An important part of these numerical investigations concerns drainage, which is crucial to the accuracy of the simulation results. To overcome the difficulty of obtaining underground pipe network data and improve the traditional equivalent drainage simulation method, an equivalent drainage model based on manholes is proposed, to simulate urban flooding based on a two-dimensional hydrodynamic model. The new model is applied to a classic case and the simulation results are compared with those from the MIKE URBAN model to verify the simulation accuracy of the proposed formulation. The submerged areas given by the two models are compared under different rainfall conditions, with an average relative error of 6%. The differences in water depths at various nodes are statistically analyzed, and the average Nash–Sutcliffe efficiency and average root mean square error are found to be 96% and 0.03 m, respectively. The results of this research provide effective urban flood simulation in areas lacking pipe network data and have important significance for promoting the practical application of refined numerical simulations of urban flooding.

Study on the response analysis of LID hydrological process to rainfall pattern based on framework for dynamic simulation of urban floods

An in-depth analysis of the urban flood disaster level in response to different rainfall characteristics and Low Impact Development (LID) measures is of significant importance for addressing unfavorable management conditions and implementing effective flood control measures. This study proposes a dynamic urban flood simulation framework based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS) spatial analysis, incorporating an active inundation seed search algorithm. The framework is calibrated and validated using nine historical urban flood events. Subsequently, the impact of rainfall patterns on urban inundation under LID measures is analyzed based on the dynamic urban flood simulation framework. The results show that the urban flood simulation framework exhibits good applicability, with Nash-Sutcliffe Efficiency (NSE) values of 0.825 and 0.763 during the calibration and validation periods, respectively. The extent of inundation shows little variation for rainfall events with a return period greater than 20 years, and the location of flooding is minimally affected by rainfall patterns. LID measures have a decreasing effect on urban inundation control as the return period of rainfall increases, and there are variations in hydrological responses to different rainfall patterns under the same return period. For single-peak rainfall events with the same return period, the control rates of inundation volume, flow, and infiltration decrease as the rainfall peak coefficient increases, indicating a weakening effect of LID measures on flood control with increasing rainfall peak coefficient. Under the same return period conditions, LID measures exhibit the best runoff control effect for uniform rainfall, while their effectiveness is lower for double-peak rainfall events and single-peak rainfall events with an r = 0.75 coefficient. The findings of this study provide a theoretical basis for urban flood warning and management of Low Impact Development measures.

Experimental study on the inundation characteristics of flooding in a long straight subway tunnel

In recent years, the increase in extreme urban flooding events has caused severe property damage and safety problems. Subway tunnels are particularly vulnerable to underground space flooding. There have been traditional hydraulic experiments conducted to investigate unsteady turbulence properties of free surface flow in open channels. However, most experiments in flumes and pipes do not focus on the early formation process of inundation considering the unique structural configurations of subway tunnels. In this study, the general pattern that floodwater intrudes into a subway tunnel was studied by a scaled model experiment. Under different conditions of tunnel slope and inlet water discharge, the flood flow pattern, the water elevation, and flow velocity were investigated and analyzed. The results show that the flooding domain could be divided into three regions, including a Forming Region, a Uniform Region, and a Front Region. The unsteady Front Region performs an exponential rise in water elevation, and the velocity of flood propagation along the tunnel is nearly constant. The shape of the water surface profile is mainly influenced by tunnel slope, and the effect has a transition at the critical tunnel slope which was measured to be around 3‰. The classifications of flooding behavior under different conditions are described in detail. An empirical nondimensionalized formula for the tunnel inundation process in both unsteady and steady stages was proposed. This model enables rapid calculation of water depths with time in a subway tunnel. This research could provide an understanding of flood propagation in subway tunnels and facilitate the study of flooding detection and warnings in underground space. It also provides reference for evacuation decisions and subway flood control design.

Multi-Dimensional Urban Flooding Impact Assessment Leveraging Social Media Data: A Case Study of the 2020 Guangzhou Rainstorm

In the contexts of global climate change and the urbanization process, urban flooding poses significant challenges worldwide, necessitating effective rapid assessments to understand its impacts on various aspects of urban systems. This can be achieved through the collection and analysis of big data sources such as social media data. However, existing literature remains limited in terms of conducting a comprehensive disaster impact assessment leveraging social media data. This study employs mixed-methods research, a synergy of statistical analysis, machine learning algorithms, and geographical analysis to examine the impacts of urban flooding using the case of the 2020 Guangzhou rainstorm event. The result show that: (1) analyzing social media content enables monitoring of the development of disaster situations, with varied distributions of impact categories observed across different phases of the urban flood event; (2) a lexicon-based approach allows for tracking specific sentiment categories, revealing differential contributions to negative sentiments from various impact topics; (3) location information derived from social media texts can unveil the geographic distribution of impacted areas, and significant correlations are indicated between the waterlogging hotspots and four predisposing factors, namely precipitation, proportion of built-up surfaces, population density, and road density. Consequently, this study suggests that collecting and analyzing social media data is a reliable and feasible way of conducting rapid impact assessment for disasters.

Risk assessment of metro tunnel evacuation in devastating urban flooding events

Due to the increased frequency and magnitude of urban flooding events, there is an urgent need to study evacuation safety and disaster risk reduction in inundated urban underground spaces. In the literature, there is still a lack of methodologies to assess flooding risk in urban underground areas, which can account for the mutual effects of hydrodynamic characteristics, fluid-human interactions, and human behaviors. This study presents a hybrid model to investigate flood-induced evacuation safety in a metro tunnel based on the coupling of the hydrodynamic module, the human instability module, and the human behavior module. Firstly, floodwater conditions and flow characteristics are calculated by the hydrodynamic module. It is found that evacuation speed decreases with an increase in flow velocity and water depth. The water depth obviously increases after the flow passes the train where the flow velocity decreases with an increase in pressure due to the inference of the obstacle. Secondly, both depth-averaged velocity and depth of floodwater are used as the input parameters for the behavior module and stability module, and the flood hazard regimes for children and adults are then determined in an inundated metro tunnel. The relatively safe evacuation moving speeds for a child and an adult is suggested to be less than 0.5 m/s and 0.6 m/s, respectively. This study provides a novel approach to predicting the evacuation process and supports an early warning system in flood-prone underground areas for sustainable cities.

Real-time model predictive control of urban drainage system in coastal areas

In the context of global climate change and urbanization, model predictive control (MPC) has been increasingly applied in the field of real-time control of urban drainage systems to cope with frequent urban flooding events caused by extreme rainstorms. However, the computation time of the predictive model calculation greatly hinders further development of MPC in this field. In this study, we propose two surrogate models, namely the water tank-water balance model 1 (TWBM1) and the water tank-water balance model 2 (TWBM2), to replace the storm water management model (SWMM) as the prediction model for MPC, thus improving MPC computational efficiency. Both surrogate models can obtain the total outflow process at the downstream outfall of the study area and the corresponding overflow process. In comparison with TWBM1, TWBM2 requires 26 % fewer calibration parameters, and its structure is simpler. These results have been verified in the real-time optimal scheduling of a rainwater drainage system in the Doumen area of Fuzhou City, China. The results show that TWBM1 and TWBM2 have shorter computation time compared to SWMM, which improves the efficiency of MPC computation. Additionally, in comparison with the existing scheduling results, MPC has better performance in terms of economy, security and computational efficiency.

Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways

Driven by the change in intense land use and land cover (LULC) due to fast urbanization, urban flooding events have become the most frequent and influential hazards over the last few decades. Accurately predicting possible flood-prone locations under the dynamic fluctuations of LULC is crucial for sustainable urban development. However, there has been sparse studies on systematic integration of LULC changes into anticipate urban development scenarios coupled with flooding vulnerability assessment. Therefore, this study proposed a robust and powerful cascade modeling chain consisting of Maximum Entropy, System Dynamics and Patch-generating Land Use Simulation in combination with shared socio-economicpathwaysfor projecting temporal and spatial dynamic changes associated with urban flooding vulnerability. Taking Guangdong Hong Kong Macao Greater Bay Area (GBA) as case study, the results showed that the increase in urban flooding was largely caused by the expansion of construction land. Overall, a substantial distinction within the scenarioswas observed and the flooding vulnerability was ranked in the order of SSP126 < SSP245 < SSP585. Under SSP585, the areas with high flooding risk will be expected to increase significantly,accounting for26% of the total areas,and nearly half of the built-upareas are exposed to flooding risk by 2050.Under SSP245, the built-up areas exposed tomedium and high flooding riskwere anticipated to cover nearly a fifth of the total areas.Under SSP126 scenario, nearly no change was predicted in the area with high flooding risk, with only increase of 1%,future urbanization hotspots associated with serious flooding risks will likely to be found on thefringeof the GBA’s construction areas,in line with the extent of construction land expansion.The finding of this study will shed a comprehensive insight into the identification of spatial and temporal distribution of urban flooding vulnerability to facilitate the exploration of flood mitigation measures associated with dynamic changes in urban land use.

An Integrative Framework for Assessment of Urban Flood Response to Changing Climate

AbstractIncreasing frequency of extreme rainfall induced catastrophic urban flood events in recent decades demands proactive efforts to assess flood risk and vulnerability. Here, we develop an integrative framework for fine spatiotemporal scale assessment of urban flood response to historical and future projected changes in extreme precipitation. The framework includes three main components—nonstationary modeling of historical extreme precipitation, modeling of future precipitation, and urban flood simulations. It also provides robust estimates of uncertainty in design precipitation from statistical modeling, multiple climate models and stochastic uncertainty, estimated using machine learning techniques. We demonstrate the proposed framework for White Oak Bayou watershed in Houston, Texas, US. Two‐dimensional hydrologic‐hydraulic Interconnected Channel and Pond Routing model is used to simulate flood response from design precipitation for historical (1986–2020) and two future (2021–2050 and 2071–2100) periods in three (SSP1‐2.5, SSP2‐4.5, and SSP5‐8.5) future climate scenarios. Results show that nonstationary design estimates for historical precipitation are 14%–25% higher than the stationary estimates for 100‐year event of 1‐ to 24‐hr duration. Contrary to general global trends, we found a significant reduction in future design precipitation in all three emission scenarios. Additionally, stochastic uncertainty in future design precipitations is found to be larger than the modeling uncertainty in historical estimates and climate model uncertainty in future estimates. Flood response in terms of peak flood and total flood volume suggests that the difference in stationary versus nonstationary historical and future design precipitation are substantial to cause considerable change in simulated flood.

Prediction of Future Urban Rainfall and Waterlogging Scenarios Based on CMIP6: A Case Study of Beijing Urban Area

Extreme weather events will become more frequent and severe as a result of climate change, necessitating an immediate need for cities to adapt to future climate change. Therefore, the prediction of future precipitation and waterlogging is of utmost importance. Using Beijing as an example, the simulation capability of different models was evaluated, and the optimal model for the study area was screened using Taylor diagrams and interannual variability scores, along with actual monthly precipitation data from Chinese weather stations from 1994 to 2014 and historical monthly precipitation data from 10 coupled models from Coupled Model Intercomparison Project Phase 6 (CMIP6). The SWMM model was then used to simulate future rainfall and waterlogging scenarios for the study area using precipitation forecast data for 2020–2050 from the best model to investigate the impact of climate change on future rainfall and waterlogging in urban areas. CMIP6 brings together the most recent simulation data from major climate models on a global scale, providing a broader and more diverse range of model results and thereby making future predictions more accurate and dependable, and its findings provide a theoretical foundation for the emergency management of and scientific responses to urban flooding events. The following major conclusions were reached: 1. The best-performing models are EC-Earth3, GFDL-ESM4, and MPI- ESM1-2-HR. EC-Earth3 is a modular Earth system model developed collaboratively by a European consortium. MPI-ESM1-2 is a climate precipitation prediction model developed in Germany and promoted for global application, whereas the GFDL-ESM4 model was developed in the United States and is currently employed for global climate precipitation simulations. 2. Under future climate circumstances, the total annual precipitation in the example region simulated by all three models increases by a maximum of 40%. 3. Under future climatic conditions, urban surface runoff and nodal overflow in the study area will be more significant. The node overflow will become more severe with the increase in climate scenario oppression, and the potential overflow nodes will account for 1.5%, 2.7%, and 2.9% of the total number of nodes under the SSP1–2.6, SSP2–4.5, and SSP5–8.5 scenarios, respectively. 4. In the future, the effectiveness of stormwater drainage systems may diminish. To increase climate change resilience, the impacts of climate change should be considered when planning the scope of stormwater optimization and the integrated improvement of gray–green–blue facilities.

Resilience effect of decentralized detention system to extreme flooding events

Abstract The experiences from recent urban flooding events suggest that the conventional centralized detention systems based on detention size expansion with minimum costs need to accommodate the extreme flooding events exceeding their design capacity. This study investigated the resilience effects of decentralized detention systems to address extreme flooding events in urban areas. An integrated flood resilience measure that integrates the regional performance of sub-basins with a decentralized flood control system was proposed and applied to an urban watershed including five sub-basins. Hydrologic and hydraulic analysis to quantify the hydrologic responses and resilience of the watershed to flooding scenarios was performed using the Hydrological Modeling System and Geospatial River Analysis System of Hydrological Engineering Centre models. The results identified the best locations of distributed detention ponds and characterized the effects of the decentralization levels of the detention system on enhancing flood resilience to extreme events. Additionally, an increase in the resilience effects of the decentralized detention system was found in the cases of more extreme flooding events from the combined impacts of high-intensity rainfall and land cover change. The findings suggest insights into incorporating decentralization strategies into decision-making on detention systems to resiliently cope with extreme flooding events in urban watersheds.

Effect of urban neighbourhood layout on the flood intrusion rate of residential buildings and associated risk for pedestrians

Urban neighbourhood layouts affect the flood intrusion rate towards residential buildings through various kinds of damaged openings (doors, windows, gates and shafts) and the associated risk for pedestrians within the urban building complex and ultimately the safety of indoor and outdoor pedestrians in urban flooding events. This study adopts computational fluid dynamics (CFD) to quantify the flood intrusion discharge towards residential buildings and evaluate the flood hazard rating of pedestrians within urban areas of divergent configurations in conjunction with a laboratory flume experiment regarding the stability/instability behaviour of a dummy model that represents a human body in a quasi-natural state. Five types of urban building layouts with three different building densities under three various floodwater conditions, including a determinant type A (see the graphic abstract hereafter), a point group type B, an enclosed type C, a hybrid type D and a new hybrid type E, are examined. The results show that the determinant type A and hybrid type D have a similar trend in terms of the flood intrusion discharge distributions towards each row of buildings and discharge variation with respect to urban building spacing, whereas other urban layouts exhibit different characteristics. Strong floodwater leads to an increased flood risk for pedestrians both outdoors and even indoors within various urban configurations: pedestrians are the most prone to toppling instability on the streets, in turn, behind the building and within the interior of the building. For both determinant type A and hybrid type D, the more pedestrians lie behind the building approaching the upstream, the safer the pedestrians become, while for point group type B, enclosed type C and new hybrid type E, the trend is reversed. The experimental results also offer a novel valuable dataset for the validation of other urban flooding numerical models. This study could be referred to by local authorities and stakeholders to take effective measures to improve the safety of pedestrians and mitigate flood risk in the context of urban planning and urban flood emergency management, such as strengthening flood-resilient urban facility designs and devising optimal routines for emergent evacuation of outdoor pedestrians.

Risk assessment of individuals exposed to urban floods

Risk assessment of individuals in urban flooding events requires quantitative criteria of human instabilities and hazard identifications. The existing guidelines might not be representative of some inundation situations in urban settings without considering the risks associated with human behaviors. Current guidelines may be under or over-estimated to be applied during a natural urban flooding disaster. This work proposes a hybrid model by coupling an efficient hydrodynamic model with a physics-based model to quantify individuals’ instabilities in urban flood events, both children and adults, based on the flow regimes and human physical characteristics. The model accounts for human behavior and the failure mechanism caused by physical instabilities of toppling, sliding, and drowning. The hazard degree is identified in terms of flow velocity and water depth in floodwaters. The hydrodynamic model is calibrated in a densely urbanized area in the city of the UK and the instabilities threshold curves for adults and children are validated using existing measurements in the literature. Applications of extreme precipitation events that occurred in Wuhan city of China showed that adults are relatively safe in most of the study sites with a low hazard except in the areas where the flood water depth exceeds the low limit of hazard criteria. However, the distribution of flood hazard degrees for children is significantly increased compared to that for adults. Furthermore, hazardous zones were observed in the vicinity of critical facilities such as the government house, supermarket, hospital, metro station, and primary school, consistent with the deep flood depth distribution. Overall, this work develops a hybrid model for risk assessment of individuals exposed to urban floods and the hazard criteria that could be implemented in urban flood risk management for sustainable cities.

Spatial-temporal evolution of influencing mechanism of urban flooding in the Guangdong Hong Kong Macao greater bay area, China

Extreme weather has been more frequent in recent years. Urban agglomerations, as areas with a high density of human activities, have been plagued by storm flooding. Historically, the main focus of attention on flood control in urban agglomerations has gradually shifted from underground pipe networks to the impervious surface, reflecting profound changes in the influencing mechanism of urban flooding. Exploring the evolution of the mechanisms influencing urban flooding in the Guangdong Hong Kong Macao Greater Bay Area (GBA) urban agglomeration is of great reference significance for formulating flood prevention and control measures and promoting high-quality development of the GBA city cluster. In this paper, we fully use the collected information on urban flooding events from 1980 to 2018 in the GBA city cluster. Correlation analysis and geographically weighted regression (GWR) are used to analyze the influence of impervious surface percentage (ISP), impervious surface aggregation index (AI), impervious surface mean shape index (Shape_MN), vegetation cover (FVC), water surface ratio (WSR), relative elevation (RE) and slope on flooding in urban clusters and their evolution characteristics over time from a global perspective and spatial heterogeneity, respectively. The results show that: 1) ISP, AI, Shape_MN, and WSR are positively correlated with urban flooding, while FVC, RE, and Slope are negatively correlated with urban flooding. The correlations of each factor showed a general trend of gradual strengthening over time, and the increase rate slowed down after 2000, while the correlation of WSR showed a relatively noticeable decrease. 2) The GWR results show that each factor’s influence on urban flooding has pronounced spatial-temporal heterogeneity, and each factor shows different distribution characteristics. This study uses long time series of urban flooding point data to explore the spatial-temporal evolution of the influencing mechanism of urban flooding in the GBA urban agglomeration. We hope to provide a scientific basis for an in-depth understanding of the causes of urban flooding in the GBA, intending to provide auxiliary decision-making support for the formulation of waterlogging prevention and control measures.